Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma.

Journal Article (Journal Article)

BACKGROUND AND PURPOSE: Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes. MATERIALS AND METHODS: We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features. RESULTS: From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI. CONCLUSIONS: Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.

Full Text

Duke Authors

Cited Authors

  • Zhang, M; Wong, SW; Lummus, S; Han, M; Radmanesh, A; Ahmadian, SS; Prolo, LM; Lai, H; Eghbal, A; Oztekin, O; Cheshier, SH; Fisher, PG; Ho, CY; Vogel, H; Vitanza, NA; Lober, RM; Grant, GA; Jaju, A; Yeom, KW

Published Date

  • September 2021

Published In

Volume / Issue

  • 42 / 9

Start / End Page

  • 1702 - 1708

PubMed ID

  • 34266866

Pubmed Central ID

  • PMC8423034

Electronic International Standard Serial Number (EISSN)

  • 1936-959X

Digital Object Identifier (DOI)

  • 10.3174/ajnr.A7200


  • eng

Conference Location

  • United States